New study conducted by an international team of researchers suggests that artificial intelligence (AI) may be better than highly-trained humans at detecting certain skin cancers

Artificial intelligence (AI) has been working its way into health technology for several years and, so far, AI tools have been a boon to physicians and health networks. Until now, though, the general view was that it was a supplemental tool for diagnosticians, not a replacement for them. But what if the AI was better at detecting disease than humans, including anatomic pathologists?

Researchers in the Department of Dermatology at Heidelberg University in Germany have concluded that AI can be more accurate at identifying certain cancers. The challenge they designed for their study involved skin biopsies and dermatologists.

They pitted a deep-learning convolutional neural network (CNN) against 58 dermatologists from 17 countries to determine which was more accurate at detecting malignant melanomas—humans or AI. A CNN is an artificial network based on the biological processes that occur when neurons in the brain are connected to each other and respond to what the eye sees.

The CNN won.

“For the first time we compared a CNN’s diagnostic performance with a large international group of 58 dermatologists, including 30 experts. Most dermatologists were outperformed by the CNN. Irrespective of any physicians’ experience, they may benefit from assistance by a CNN’s image classification,” the report noted.

“I expected only a performance on an even level with the physicians. The outperformance even of the average experienced and trained dermatologists was a major surprise,” Holger Haenssle, PhD, Professor of Dermatology at Heidelberg University and one of the authors of the study, told Healthline. Anatomic pathologists will want to follow the further development of this research and its associated diagnostic technologies. (Photo copyright: University of Heidelberg.)

Does AI Tech Have Superior Visual Acuity Compared to Human Eyes?

The dermatologists who participated in the study had varying degrees of experience in dermoscopy, also known as dermatoscopy. Thirty of the doctors had more than five-year’s experience and were considered to be expert level. Eleven of the dermatologists were considered “skilled” with two- to five-year’s experience. The remaining 17 doctors were termed beginners with less than two-year’s experience.

To perform the study, the researchers first compiled a set of 100 dermoscopic images that showed melanomas and benign moles called Nevi. Dermoscopes (or dermatoscopes) create images using a magnifying glass and light source pressed against the skin. The resulting magnified, high-resolution images allow for easier, more accurate diagnoses than inspection with the naked eye.

During the first stage of the research, the dermatologists were asked to diagnose whether a lesion was melanoma or benign by looking at the images with their naked eyes. They also were asked to render their opinions for any needed action, such as surgery and follow-up care based on their diagnoses.

After this part of the study, the dermatologists on average identified 86.6% of the melanomas and 71.3% of the benign moles. More experienced doctors identified the melanomas at 89%, which was slightly higher than the average of the group.

The researchers also showed 300 images of malignant and benign skin lesions to the CNN. The AI accurately identified 95% of the melanomas by analyzing the images.

“The CNN missed fewer melanomas, meaning it had a higher sensitivity than the dermatologists, and it misdiagnosed fewer benign moles as malignant melanoma, which means it had a higher specificity. This would result in less unnecessary surgery,” Haenssle told CBS News.

In a later part of the research, the dermatologists were shown the images a second time and provided clinical information about the patients, including age, gender, and location of the lesion. They were again instructed to make diagnoses and projected care decisions. With the additional information, the doctors’ average detection of melanomas increased to 88.9% and their recognition of benign moles increased to 75.7%. Still below the results of the CNN.

These findings suggest that the visual pattern recognition of AI technology could be a meaningful tool to help physicians and researchers diagnose certain cancers.

“In the future, I think AI will be integrated into practice as a diagnostic aide, particularly in primary care, to support the decision to excise a lesion, refer, or otherwise to reassure that it is benign,” Victoria Mar, PhD, an Adjunct Senior Lecturer in the Department of Public Health and Preventative Medicine at Australia’s Monash University, told Healthline.

“There is the potential for AI technology to be integrated with 2D or 3D skin imaging systems, which means that the majority of benign lesions would be already filtered by the machine, so that we can spend more time concentrating on the difficult or more concerning lesions,” she said. “To me, this means a more productive interaction with the patient, where we can focus on appropriate management and provide more streamlined care.”

AI Performs Well in Other Studies Involving Skin Biopsies

This study is not the only research that suggests entities besides humans may be utilized in diagnosing some cancers from images. Last year, computer scientists at Stanford University performed similar research and found comparable results. For that study, the researchers created and trained an algorithm to visually diagnose potential skin cancers by looking at a database of skin images. They then showed photos of skin lesions to 21 dermatologists and asked for their diagnoses based on the images. They found the accuracy of their AI matched the performance of the doctors when diagnosing skin cancer from viewed images.

While many dermatologists read patient biopsies on their own, they also refer high volumes of skin biopsies to anatomic pathologists. A technology that can accurately diagnose skin cancers could potentially impact the workload received by clinical laboratories and anatomic pathology groups.